'''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
    Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''

import torch
import torch.nn as nn
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class Flatten(nn.Module):
    def __init__(self):
        super(Flatten,self).__init__()
    def forward(self,x):
        shape = torch.prod(torch.tensor(x.shape[1:])).item()
        return x.reshape(-1,shape)

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
    # while original implementation places the stride at the first 1x1 convolution(self.conv1)
    # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
    # This variant is also known as ResNet V1.5 and improves accuracy according to
    # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.

    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class ResNetCifar10(nn.Module):

    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None,args = None):
        super(ResNetCifar10, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group

        self.dataset = args.dataset
        # if self.dataset == 'widar':
        #     args.num_channels = 3
        #     self.reshape = nn.Sequential(
        #         nn.ConvTranspose2d(22,args.num_channels, 7, stride=1),
        #         nn.ReLU(),
        #         nn.ConvTranspose2d(args.num_channels, args.num_channels, kernel_size=7, stride=1),
        #         nn.ReLU()
        #     )


        self.layer0_conv1 = nn.Conv2d(args.num_channels, self.inplanes, kernel_size=3, stride=1, padding=1,
                               bias=False)
        self.layer0_bn1 = norm_layer(self.inplanes)
        self.layer0_relu = nn.ReLU(inplace=True)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                       dilate=replace_stride_with_dilation[2])
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.flatten = Flatten()
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        # for m in self.modules():
        #     if isinstance(m, nn.Conv2d):
        #         nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
        #     elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
        #         nn.init.constant_(m.weight, 1)
        #         nn.init.constant_(m.bias, 0)

        # # Zero-initialize the last BN in each residual branch,
        # # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        # if zero_init_residual:
        #     for m in self.modules():
        #         if isinstance(m, Bottleneck):
        #             nn.init.constant_(m.bn3.weight, 0)
        #         elif isinstance(m, BasicBlock):
        #             nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))

        return nn.Sequential(*layers)

    def _forward_impl(self, x):
        # See note [TorchScript super()]
        # if self.dataset == 'widar':
        #     x = self.layer0_conv1(self.reshape(x))
        # else:
        #     x = self.layer0_conv1(x)
        x = self.layer0_conv1(x)
        x = self.layer0_bn1(x)
        x = self.layer0_relu(x)
        # print(x.size())
        # print(x.size()[0])
        result = {'representation0' : x}
        x = self.layer1(x)
        result['representation1'] = x
        # print(x.size())
        # print(x.size()[0])
        x = self.layer2(x)
        result['representation2'] = x
        # print(x.size())
        # print(x.size()[0])
        x = self.layer3(x)
        result['representation3'] = x
        x = self.layer4(x)
        result['representation4'] = x
        x = self.avgpool(x)
        # x = torch.flatten(x, 1)
        x = self.flatten(x)
        result['representation'] = x
        x = self.fc(x)
        result['output'] = x

        return result

    def mapping(self, z_input, start_layer_idx=-1, logit=True):
        z = z_input
        z = self.fc(z)

        result = {'output': z}
        if logit:
            result['logit'] = z
        return result

    def forward_level(self, x, start_layer_idx=0):
        # See note [TorchScript super()]
        if start_layer_idx <= 0:
            out0 = self.relu(self.bn1(self.conv1(x)))
        else:
            out0 = x
        if start_layer_idx <= 1:
            out1 = self.layer1(out0)
        else:
            out1 = out0
        if start_layer_idx <= 2:
            out2 = self.layer2(out1)
        else:
            out2 = out1
        if start_layer_idx <= 3:
            out3 = self.layer3(out2)
        else:
            out3 = out2
        if start_layer_idx <= 4:
            out4 = self.layer4(out3)
            out4 = self.avgpool(out4)
            out4 = out4.view(out4.size(0), -1)
        else:
            out4 = out3

        logit = self.fc(out4)

        return logit


    def forward(self, x, start_layer_idx=0, logit=False):
        if start_layer_idx < 0:  #
            return self.mapping(x, start_layer_idx=start_layer_idx, logit=logit)
        elif start_layer_idx != 0:
            return self.forward_level(x, start_layer_idx)

        return self._forward_impl(x)




# def ResNet8(**kwargs):
#     return ResNetCifar10(BasicBlock, [1, 1, 0,1], **kwargs)

def ResNet18_cifar10(**kwargs):
    r"""ResNet-18 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return ResNetCifar10(BasicBlock, [2, 2, 2, 2], **kwargs)

def ResNet34_cifar10(**kwargs):
    r"""ResNet-34 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return ResNetCifar10(BasicBlock, [3, 4, 6, 3], **kwargs)



def ResNet50_cifar10(**kwargs):
    r"""ResNet-50 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return ResNetCifar10(Bottleneck, [3, 4, 6, 3], **kwargs)


    



